Neuro-response data synchronization

ABSTRACT

Efficient and effective mechanisms for collecting electroencephalography (EEG) data are provided to synchronize neuro-response data collection with stimulus material presentation for in situ engagement monitoring and tracking. An EEG headset includes multiple point electrodes individually isolated and amplified. In some examples, a stimulus material presentation mechanism includes a clock source and a clock transmitter. The clock transmitter sends clock signals to a neuro-response data collection mechanism to allow synchronization of neuro-response data collected with stimulus presentation events. The EEG headset can be configured to perform processing while supporting both continuous input and output.

RELATED APPLICATIONS

This patent is related to U.S. patent application Ser. Nos. 12/056,190;12/056,211; 12/056,221; 12/056,225; 12/113,863; 12/113,870; 12/122,240;12/122,253; 12/122,262; 12/135,066; 12/135,074; 12/182,851; 12/182,874;12/199,557; 12/199,583; 12/199,596; 12/200,813; 12/234,372; 12/135,069;12/234,388; 12/544,921; 12/544,934; 12/546,586; 12/544,958; 12/846,242;12/410,380; 12/410,372; 12/413,297; 12/545,455; 12/608,660; 12/608,685;13/444,149; 12/608,696; 12/731,868; 13/045,457; 12/778,810; 13/104,821;13/104,840; 12/853,197; 12/884,034; 12/868,531; 12/913,102; 12/853,213;and 13/105,774.

TECHNICAL FIELD

The present disclosure relates to portable electroencephalography (EEG)headsets and stimulus synchronization.

DESCRIPTION OF RELATED ART

Conventional electroencephalography (EEG) systems use scalp levelelectrodes typically attached to elastic caps or bands to monitorneurological activity. Conductive gels and pastes are applied beforeplacement of the scalp electrodes to improve sensitivity. However,application of conductive gels and pastes is often inconvenient and timeconsuming. Furthermore, conductive gels and pastes can often bleedbetween neighboring electrodes and cause signal contamination. Elasticcaps or bands can also be uncomfortable for prolonged use. Conventionalmechanisms are often used in highly controlled laboratory environmentsunder supervision of trained technicians.

Some efforts have been made in the development of more portable,efficient, and effective EEG data collection mechanisms. However,available mechanisms have a variety of limitations. Consequently, it isdesirable to provide improved mechanisms for collecting EEG data.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular example embodiments.

FIG. 1 illustrates one example of a system for performing neuro-responsedata synchronization.

FIGS. 2A-2E illustrate a particular example of a neuro-response datacollection mechanism. In the examples shown, the example neuro-responsedata collection mechanism includes electrodes connected to hubs on thesides of the data collection mechanism, which are rotatable, forexample, between the position shown in FIG. 2C and the position shown inFIG. 2E.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with theneuro-response collection system.

FIG. 5 illustrates one example of a report generated using theneuro-response collection system.

FIG. 6 illustrates one example of a technique for performingneuro-response data synchronization.

FIG. 7 provides one example of a system that can be used to implementone or more mechanisms.

DESCRIPTION OF PARTICULAR EMBODIMENTS

Reference will now be made in detail to some specific examples of theinvention including the best modes contemplated by the inventors forcarrying out the invention. Examples of these specific embodiments areillustrated in the accompanying drawings. While the invention isdescribed in conjunction with these specific embodiments, it will beunderstood that it is not intended to limit the invention to thedescribed embodiments. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claims.

For example, the techniques and mechanisms of the present invention willbe described in the context of particular types of electrodes. However,it should be noted that the techniques and mechanisms of the presentinvention apply to a variety of different types of electrodes andcontacts. In the following description, numerous specific details areset forth in order to provide a thorough understanding of the presentinvention. Particular example embodiments of the present invention maybe implemented without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the present invention.

Various techniques and mechanisms of the present invention willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present inventionunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present invention will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

Efficient and effective mechanisms for collecting electroencephalography(EEG) data are provided to synchronize neuro-response data collectionwith stimulus material presentation for in situ engagement monitoringand tracking. An EEG headset includes multiple point electrodesindividually isolated and amplified. In some examples, a stimulusmaterial presentation mechanism includes a clock source and a clocktransmitter. The clock transmitter sends clock signals to aneuro-response data collection mechanism to allow synchronization ofneuro-response data collected with stimulus presentation events. The EEGheadset can be configured to perform processing while supporting bothcontinuous input and output.

Example Embodiments

Conventional distributed response monitoring mechanisms merely trackstimulus being viewed and rely on behavior and survey based datacollected from subjects exposed to stimulus materials. In someinstances, attempts are made to measure responses to programs andcommercials using demographic, statistical, user behavioral, and surveybased information. For example, subjects are required to completesurveys after exposure to programs and/or commercials. However, surveyresults often provide only limited information about program andcommercial response. For example, survey subjects may be unable orunwilling to express their true thoughts and feelings about a topic, orquestions may be phrased with built in bias. Articulate subjects may begiven more weight than non-expressive ones. Analysis of multiple surveyresponses and correlation of the responses to stimulus material is alsolimited. A variety of semantic, syntactic, metaphorical, cultural,social and interpretive biases and errors prevent accurate andrepeatable evaluation. Mechanisms for storing, managing, and retrievingconventional responses are also limited.

Consequently, the techniques and mechanisms of the present invention useEEG measurements to allow more accurate measurement and monitoring ofattention and engagement. According to various embodiments, an EEGheadset is provided to subjects for use home, recreational, work, aswell as laboratory environments. In particular embodiments, the EEGheadset includes multiple dry electrodes individually isolated andamplified. Data from individual electrodes may be processed prior tocontinuous transmission to a data analyzer. Processing may includefiltering to remove noise and artifacts as well as compression and/orencryption. Individual electrodes are configured to contact the scalp ina variety of areas while avoiding the contact with the temporal region.

According to various embodiments, an electric cap or band is notrequired because individual opposing electrodes are attached to exertsomewhat opposing forces to secure a headset. In particular embodiments,a headset spring mechanism exerts elastic forces to push both frontaland rear electrodes into close contact with the scalp. According tovarious embodiments, frontal electrodes exert point forces thatcounterbalance point forces exerted by rear electrodes. Electrodes areshaped as points to reach the scalp through non-conductive hairfollicles. One of more elastic mechanisms can be used to allow foreffective counterbalancing forces. In particular embodiments, right sidescalp electrodes counterbalance forces from left side scalp electrodesto secure a headset, allowing front electrodes and rear electrodes tocontact the scalp. It should be noted that forces need not perfectlycounterbalance.

EEG dry electrodes allow in situ monitor and tracking of neuro-responseactivity including engagement levels. According to various embodiments,the data collection mechanism is synchronized with stimulus material toallow determination of aspects of stimulus materials that evokeparticular neurological responses. In particular embodiments, the EEGheadset is synchronized with stimulus data using a shared clock or anexternal clock from a cell tower or a satellite. Although a headset maymerely have an internal clock that generates timestamps, it isrecognized that timestamps in themselves are insufficient to provide forthe precise measurements used to determine subject neurologicalresponses.

According to various embodiments, a stimulus material presentationmechanism uses a clock source to transmit clock signals to an EEGheadset. The clock source may be an external clock, timing informationembedded in a stimulus material presentation stream, a device clock,etc. In particular embodiments, the EEG headset stores neuro-responsedata collected from a user exposed to stimulus material for transmissionto a data analyzer. Neuro-response data is synchronized with timinginformation associated with the stimulus material presentation to allowidentification of responses and associated events in the neuro-responsedata. In some embodiments, neuro-response data is stored withsynchronized timing data to allow placement of stimulus material andneuro-response data on the same time scale.

According to various embodiments, the EEG headset uses flexible printedcircuit boards (PCBs) to enhance shielding, routability andconnectability of elements including amplifiers, sensors, transmitters,etc.

A subject may wear the portable neuro-response data collection mechanismduring a variety of activities in non-laboratory settings. This allowscollection of data from a variety of sources while a subject is in anatural state. In particular embodiments, data collection can occureffectively in corporate and laboratory settings, but it is recognizedthat neuro-response data may even be more accurate if collected while asubject is in a more natural environment.

A variety of neurological, neuro-physiological, and effector mechanismsmay be integrated in a neuro-response data collection mechanism. EEGmeasures electrical activity associated with post synaptic currentsoccurring in the milliseconds range. Subcranial EEG can measureelectrical activity with the most accuracy, as the bone and dermallayers weaken transmission of a wide range of frequencies. Nonetheless,surface EEG provides a wealth of electrophysiological information ifanalyzed properly. Portable EEG with dry electrodes provide a largeamount of neuro-response information. It should be recognized that othermechanisms such as Electrooculography (EOG), eye tracking, facialemotion encoding, reaction time, Functional Magnetic Resonance Imaging(fMRI) and Magnetoencephalography (MEG) can also be used in particularcircumstances.

According to various embodiments, the techniques and mechanisms of thepresent invention intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyallow monitoring.

According to various embodiments, subjects may be exposed topredetermined or preselected stimulus material. In other examples, nopredetermined or preselected stimulus material is provided and a systemcollects neuro-response data for stimulus material a user is exposed toduring typical activities.

For example, multiple subjects may be provided with portable EEGmonitoring systems with dry electrodes that allow monitoring ofneuro-response activity while subjects view billboards. Response data isanalyzed and integrated. In some examples, all response data is providedfor data analysis. In other examples, interesting response data alongwith recorded stimulus material is provided to a data analyzer.According to various embodiments, response data is analyzed and enhancedfor each subject and further analyzed and enhanced by integrating dataacross multiple subjects.

According to various embodiments, individual and integrated responsedata is numerically maintained or graphically represented. Measurementsfor multiple subjects are analyzed to determine possible patterns,fluctuations, profiles, etc.

According to various embodiments, neuro-response data may showparticular effectiveness of stimulus material for a particular subset ofindividuals. A variety of stimulus materials such as entertainment andmarketing materials, media streams, billboards, print advertisements,text streams, music, performances, sensory experiences, etc. can beanalyzed. According to various embodiments, enhanced neuro-response datais generated using a data analyzer that performs both intra-modalitymeasurement enhancements and cross-modality measurement enhancements.According to various embodiments, brain activity is measured not just todetermine the regions of activity, but to determine interactions andtypes of interactions between various regions. The techniques andmechanisms of the present invention recognize that interactions betweenneural regions support orchestrated and organized behavior. Attention,emotion, memory, retention, priming, and other characteristics are notmerely based on one part of the brain but instead rely on networkinteractions between brain regions.

The techniques and mechanisms of the present invention further recognizethat different frequency bands used for multi-regional communication canbe indicative of the effectiveness of stimuli. In particularembodiments, evaluations are calibrated to each subject and synchronizedacross subjects. In particular embodiments, templates are created forsubjects to create a baseline for measuring pre and post stimulusdifferentials. According to various embodiments, stimulus generators areintelligent and adaptively modify specific parameters such as exposurelength and duration for each subject being analyzed.

FIG. 1 illustrates one example of a system for collection ofneuro-response data. Subjects 131, 133, 135, and 137 are associated withneuro-response data collection mechanisms 141, 143, 145, and 147.According to various embodiments, subjects voluntarily useneuro-response data collection mechanisms such as EEG caps, EOG sensors,recorders, cameras, etc., during exposure to particular stimulusmaterials provided by stimulus presentation mechanism 101 or duringnormal activities in non-laboratory environments. According to variousembodiments, neuro-response data is measured for subjects innon-laboratory settings including homes, shops, workplaces, parks,theatres, etc. In particular embodiments, neuro-response data collectionmechanisms 145 and 147 include persistent storage mechanisms and network161 interfaces that are used to transmit collected data to a dataanalyzer 181. In other examples, neuro-response data collectionmechanisms 141 and 143 include interfaces to computer systems 151 and153 that are configured to transmit data to a data analyzer 181 over oneor more networks. According to various embodiments, stimulus material isclock synchronized with the data collection mechanisms 141, 143, 145,and 147. In particular embodiments, stimulus material presentationmechanism 101 and the data collection mechanisms 141, 143, 145, and 147are clock synchronized using a clock source 103 and a clock signaltransmitter 105. The clock source 103 may be timing information embeddedin stimulus material, a cell tower or satellite clock signal, a stimuluspresentation device clock, a EEG headset clock, etc. A clock signaltransmitter 105 may be a transmitter associated with the stimulusmaterial presentation mechanism 101, a transmitter associated with theEEG headset, a cell tower or satellite, etc. According to variousembodiments, the stimulus material presentation mechanism 101 and datacollection mechanisms 141, 143, 145, and 147 also have clock signalreceivers.

Materials eliciting neuro-responses from subjects 131, 133, 135, and 137may include people, activities, brand images, information, performances,entertainment, advertising, and may involve particular tastes, smells,sights, textures and/or sounds. In some examples, stimulus material isselected for presentation to subjects 131, 133, 135, and 137. In otherexamples, stimulus material subjects are exposed to during normaleveryday activities such as driving to work or going to the grocerystore are analyzed. Continuous and discrete modes are supported.

According to various embodiments, the subjects 131, 133, 135, and 137are connected to neuro-response data collection mechanisms 141, 143,145, and 147. The data collection mechanisms 105 includes EEGelectrodes, although in some implementations may also include a varietyof neuro-response measurement mechanisms including neurological andneurophysiological measurements systems such as EOG, GSR, EKG, pupillarydilation, eye tracking, facial emotion encoding, and reaction timedevices, etc. According to various embodiments, neuro-response dataincludes central nervous system, autonomic nervous system, and/oreffector data.

The neuro-response data collection mechanisms 141, 143, 145, and 147collect neuro-response data from multiple sources. According to variousembodiments, data collection mechanisms include central nervous systemsources (EEG), autonomic nervous system sources (EKG, pupillarydilation), and effector sources (EOG, eye tracking, facial emotionencoding, reaction time). In particular embodiments, data collected isdigitally sampled and stored for later analysis. In particularembodiments, the data collected can be analyzed in real-time. Accordingto particular embodiments, the digital sampling rates are adaptivelychosen based on the neurophysiological and neurological data beingmeasured.

In one particular embodiment, the neuro-response data collectionmechanism includes EEG measurements made using scalp level electrodes,EOG measurements made using shielded electrodes to track eye data, and afacial affect graphic and video analyzer adaptively derived for eachindividual.

In particular embodiments, the data collection mechanisms 141, 143, 145,and 147 also include a condition evaluation subsystem that provides autotriggers, alerts and status monitoring and visualization components thatcontinuously monitor the status of the subject, the direction ofattention, stimulus being presented, data being collected, and the datacollection instruments. For example, the data collection mechanisms mayrecord neuro-response data while a recorder determines that a subject islistening to a particular song.

The condition evaluation subsystem may also present visual alerts andautomatically trigger remedial actions. According to variousembodiments, the data collection devices include mechanisms for not onlymonitoring subject neuro-response to stimulus materials, but alsoinclude mechanisms for identifying and monitoring the stimulusmaterials. For example, data collection mechanisms 105 may besynchronized with a set-top box to monitor channel changes. In otherexamples, data collection mechanisms 105 may be directionallysynchronized to monitor when a subject is no longer paying attention tostimulus material. In still other examples, the data collectionmechanisms 105 may receive and store stimulus material generally beingviewed by the subject, whether the stimulus is a program, a commercial,printed material, or a scene outside a window of a living room. The datacollected allows analysis of neuro-response information and correlationof the information to actual stimulus material and not mere subjectdistractions.

According to various embodiments, the neuro-response collection systemalso includes a data cleanser. In particular embodiments, the datacleanser device filters the collected data to remove noise, artifacts,and other irrelevant data using fixed and adaptive filtering, weightedaveraging, advanced component extraction (like PCA, ICA), vector andcomponent separation methods, etc. This device cleanses the data byremoving both exogenous noise (where the source is outside thephysiology of the subject, e.g. a phone ringing while a subject isviewing a video) and endogenous artifacts (where the source could beneurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various embodiments, the data cleanser device isimplemented using hardware, firmware, and/or software and may beintegrated into EEG headsets, computer systems, or data analyzers. Itshould be noted that although a data cleanser device may have a locationand functionality that varies based on system implementation.

The data cleanser can pass data to the data analyzer 181. The dataanalyzer 181 uses a variety of mechanisms to analyze underlying data inthe system to determine neuro-response characteristics associated withcorresponding stimulus material. According to various embodiments, thedata analyzer customizes and extracts the independent neurological andneuro-physiological parameters for each individual in each modality, andblends the estimates within a modality as well as across modalities toelicit an enhanced response to the stimulus material. In some examples,stimulus material recorded using images, video, or audio is synchronizedwith neuro-response data. In particular embodiments, the data analyzer181 aggregates the response measures across subjects in a dataset.

According to various embodiments, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,population distribution, as well as fuzzy estimates of attention,emotional engagement and memory retention responses.

According to various embodiments, the data analyzer 181 may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular embodiments, the intra-modality responsesynthesizer is configured to customize and extract the independentneurological and neurophysiological parameters for each individual ineach modality and blend the estimates within a modality analytically toelicit an enhanced response to the presented stimuli. In particularembodiments, the intra-modality response synthesizer also aggregatesdata from different subjects in a dataset.

According to various embodiments, the cross-modality responsesynthesizer or fusion device blends different intra-modality responses,including raw signals and signals output. The combination of signalsenhances the measures of effectiveness within a modality. Thecross-modality response fusion device can also aggregate data fromdifferent subjects in a dataset.

According to various embodiments, the data analyzer 181 also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular embodiments, blendedestimates are provided for each exposure of a subject to stimulusmaterials. According to various embodiments, numerical values areassigned to each blended estimate. The numerical values may correspondto the intensity of neuro-response measurements, the significance ofpeaks, the change between peaks, etc. Higher numerical values maycorrespond to higher significance in neuro-response intensity. Lowernumerical values may correspond to lower significance or eveninsignificant neuro-response activity. In other examples, multiplevalues are assigned to each blended estimate. In still other examples,blended estimates of neuro-response significance are graphicallyrepresented to show changes after repeated exposure.

According to various embodiments, the data analyzer 181 providesanalyzed and enhanced response data to a response integration system185. According to various embodiments, the response integration system185 combines analyzed and enhanced responses to the stimulus materialwhile using information about stimulus material attributes. Inparticular embodiments, the response integration system 185 alsocollects and integrates user behavioral and survey responses with theanalyzed and enhanced response data to more effectively measure andneuro-response data collected in a distributed environment.

According to various embodiments, the response integration system 185obtains characteristics of stimulus material such as requirements andpurposes of the stimulus material. Some of these requirements andpurposes may be obtained from a stimulus attribute repository. Othersmay be obtained from other sources. Characteristics may include viewsand presentation specific attributes such as audio, video, imagery andmessages needed, media for enhancement, media for avoidance, etc.

According to various embodiments, the response integration system 185also includes mechanisms for the collection and storage of demographic,statistical and/or survey based responses to different entertainment,marketing, advertising and other audio/visual/tactile/olfactorymaterial. If this information is stored externally, the responseintegration system 185 can include a mechanism for the push and/or pullintegration of the data, such as querying, extraction, recording,modification, and/or updating.

According to various embodiments, the response integration system 185integrates the requirements for the presented material, the assessedneuro-physiological and neuro-behavioral response measures, and theadditional stimulus attributes such as demographic/statistical/surveybased responses into a synthesized measure for various stimulus materialconsumed by users in various environments.

According to various embodiments, the response integration system 185provides stimulus and response repository 187 with data includingintegrated and/or individual stimulus material responses, stimulusattributes, synthesized measures, stimulus material, etc. A variety ofdata can be stored for later analysis, management, manipulation, andretrieval. In particular embodiments, the repository 187 could be usedfor tracking stimulus attributes and presentation attributes, audienceresponses and optionally could also be used to integrate audiencemeasurement information.

According to various embodiments, the information stored in therepository system 187 could be used to assess the audience response toprograms/advertisements in multiple regions, across multipledemographics and multiple time spans (days, weeks, months, years, etc.),determine the effectiveness of billboards, monitor neuro-responses tovideo games and entertainment, etc.

As with a variety of the components in the neuro-response collectionsystem, the response integration system can be co-located with the restof the system and the user, or could be implemented in a remotelocation. It could also be optionally separated into an assessmentrepository system that could be centralized or distributed at theprovider or providers of the stimulus material. In other examples, theresponse integration system is housed at the facilities of a third partyservice provider accessible by stimulus material providers and/or users.

FIGS. 2A-2E illustrate a particular example of a neuro-response datacollection mechanism. FIG. 2A shows a perspective view of aneuro-response data collection mechanism including multiple dryelectrodes. According to various embodiments, the neuro-response datacollection mechanism is a headset having point or teeth electrodesconfigured to contact the scalp through hair without the use ofelectro-conductive gels. In particular embodiments, each electrode isindividually amplified and isolated to enhance shielding androutability. In some examples, each electrode has an associatedamplifier implemented using a flexible printed circuit. Signals may berouted to a controller/processor for immediate transmission to a dataanalyzer or stored for later analysis. A controller/processor may beused to synchronize neuro-response data with stimulus materials. Theneuro-response data collection mechanism may also have receivers forreceiving clock signals and processing neuro-response signals. Theneuro-response data collection mechanisms may also have transmitters fortransmitting clock signals and sending data to a remote entity such as adata analyzer.

FIGS. 2B-2E illustrate top, side, rear, and perspective views of theneuro-response data collection mechanism. The neuro-response datacollection mechanism includes multiple electrodes including right sideelectrodes 261 and 263, left side electrodes 221 and 223, frontelectrodes 231 and 233, and rear electrode 251. It should be noted thatspecific electrode arrangement may vary from implementation toimplementation. However, the techniques and mechanisms of the presentinvention avoid placing electrodes on the temporal region to preventcollection of signals generated based on muscle contractions. Avoidingcontact with the temporal region also enhances comfort during sustainedwear.

According to various embodiments, forces applied by electrodes 221 and223 counterbalance forces applied by electrodes 261 and 263. Inparticular embodiments, forces applied by electrodes 231 and 233counterbalance forces applied by electrode 251. In particularembodiments, the EEG dry electrodes operate to detect neurologicalactivity with minimal interference from hair and without use of anyelectrically conductive gels. According to various embodiments,neuro-response data collection mechanism also includes EOG sensors suchas sensors used to detect eye movements.

According to various embodiments, data acquisition using electrodes 221,223, 231, 233, 251, 261, and 263 is synchronized with stimulus materialpresented to a user. Data acquisition can be synchronized with stimulusmaterial presented by using a shared clock signal. The shared clocksignal may originate from the stimulus material presentation mechanism,a headset, a cell tower, a satellite, etc. The data collection mechanism201 also includes a transmitter and/or receiver to send collectedneuro-response data to a data analysis system and to receive clocksignals as needed. In some examples, a transceiver transmits allcollected media such as video and/or audio, neuro-response, and sensordata to a data analyzer. In other examples, a transceiver transmits onlyinteresting data provided by a filter. According to various embodiments,neuro-response data is correlated with timing information for stimulusmaterial presented to a user.

In some examples, the transceiver can be connected to a computer systemthat then transmits data over a wide area network to a data analyzer. Inother examples, the transceiver sends data over a wide area network to adata analyzer. Other components such as fMRI and MEG that are not yetportable but may become portable at some point may also be integratedinto a headset.

It should be noted that some components of a neuro-response datacollection mechanism have not been shown for clarity. For example, abattery may be required to power components such as amplifiers andtransceivers. Similarly, a transceiver may include an antenna that issimilarly not shown for clarity purposes. It should also be noted thatsome components are also optional. For example, filters or storage maynot be required.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with collection of neuro-response data.According to various embodiments, a dataset data model 301 includes aname 303 and/or identifier, client attributes 305, a subject pool 307,logistics information 309 such as the location, date, and stimulusmaterial 311 identified using user entered information or video andaudio detection.

In particular embodiments, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include shopping preferences, entertainment preferences, and financialpreferences. Shopping preferences include favorite stores, shoppingfrequency, categories shopped, favorite brands. Entertainmentpreferences include network/cable/satellite access capabilities,favorite shows, favorite genres, and favorite actors. Financialpreferences include favorite insurance companies, preferred investmentpractices, banking preferences, and favorite online financialinstruments. A variety of subject attributes may be included in asubject attributes data model 315 and data models may be preset orcustom generated to suit particular purposes.

Other data models may include a data collection data model 337.According to various embodiments, the data collection data model 337includes recording attributes 339, equipment identifiers 341, modalitiesrecorded 343, and data storage attributes 345. In particularembodiments, equipment attributes 341 include an amplifier identifierand a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various embodiments, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with neuro-response data collection. According tovarious embodiments, queries are defined from general or customizedscripting languages and constructs, visual mechanisms, a library ofpreset queries, diagnostic querying including drill-down diagnostics,and eliciting what if scenarios. According to various embodiments,subject attributes queries 415 may be configured to obtain data from aneuro-informatics repository using a location 417 or geographicinformation, session information 421 such as timing information for thedata collected. Location information 423 may also be collected. In someexamples, a neuro-response data collection mechanism includes GPS orother location detection mechanisms. Demographics attributes 419 includehousehold income, household size and status, education level, age ofkids, etc.

Other queries may retrieve stimulus material recorded based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc.

Response assessment based queries 437 may include attention scores 439,emotion scores, 441, retention scores 443, and effectiveness scores 445.Such queries may obtain materials that elicited particular scores.Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various embodiments, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andneuro-response data collection measures 507. Effectiveness assessmentmeasures include composite assessment measure(s),industry/category/client specific placement (percentile, ranking, etc.),actionable grouping assessment such as removing material, modifyingsegments, or fine tuning specific elements, etc, and the evolution ofthe effectiveness profile over time. In particular embodiments,component assessment reports include component assessment measures likeattention, emotional engagement scores, percentile placement, ranking,etc. Component profile measures include time based evolution of thecomponent measures and profile statistical assessments. According tovarious embodiments, reports include the number of times material isassessed, attributes of the multiple presentations used, evolution ofthe response assessment measures over the multiple presentations, andusage recommendations.

According to various embodiments, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various embodiments, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular embodiments,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of neuro-response data collection. At601, user information is received from a subject provided with aneuro-response data collection mechanism. According to variousembodiments, the subject sends data including age, gender, income,location, interest, ethnicity, etc. after being provided with an EEGheadset including EEG electrodes.

At 603, neuro-response data is received from the subject neuro-responsedata collection mechanism. In some particular embodiments, EEG, EOG,pupillary dilation, facial emotion encoding data, video, images, audio,GPS data, etc., can all be transmitted from the subject to aneuro-response data analyzer. In particular embodiments, only EEG datais transmitted. According to various embodiments, neuro-response andassociated data is transmitted directly from an EEG cap wide areanetwork interface to a data analyzer. In particular embodiments,neuro-response and associated data is transmitted to a computer systemthat then performs compression and filtering of the data beforetransmitting the data to a data analyzer over a network.

According to various embodiments, data is also passed through a datacleanser to remove noise and artifacts that may make data more difficultto interpret. According to various embodiments, the data cleanserremoves EEG electrical activity associated with blinking and otherendogenous/exogenous artifacts. Data cleansing may be performed beforeor after data transmission to a data analyzer.

At 605, stimulus material is identified. According to variousembodiments, stimulus material is identified based on user input orsystem data. Eye tracking movements can determine where user attentionis focused at any given time. At 607, neuro-response data issynchronized with timing, location, and other stimulus material data. Inparticular embodiments, neuro-response data is synchronized with ashared clock source. According to various embodiments, neuro-responsedata such as EEG and EOG data is tagged to indicate what the subject isviewing or listening to at a particular time.

At 609, data analysis is performed. Data analysis may includeintra-modality response synthesis and cross-modality response synthesisto enhance effectiveness measures. It should be noted that in someparticular instances, one type of synthesis may be performed withoutperforming other types of synthesis. For example, cross-modalityresponse synthesis may be performed with or without intra-modalitysynthesis.

A variety of mechanisms can be used to perform data analysis 609. Inparticular embodiments, a stimulus attributes repository is accessed toobtain attributes and characteristics of the stimulus materials, alongwith purposes, intents, objectives, etc. In particular embodiments, EEGresponse data is synthesized to provide an enhanced assessment ofeffectiveness. According to various embodiments, EEG measures electricalactivity resulting from thousands of simultaneous neural processesassociated with different portions of the brain. EEG data can beclassified in various bands. According to various embodiments, brainwavefrequencies include delta, theta, alpha, beta, and gamma frequencyranges. Delta waves are classified as those less than 4 Hz and areprominent during deep sleep. Theta waves have frequencies between 3.5 to7.5 Hz and are associated with memories, attention, emotions, andsensations. Theta waves are typically prominent during states ofinternal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present inventionrecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular embodiments, EEGmeasurements including difficult to detect high gamma or kappa bandmeasurements are obtained, enhanced, and evaluated. Subject and taskspecific signature sub-bands in the theta, alpha, beta, gamma and kappabands are identified to provide enhanced response estimates. Accordingto various embodiments, high gamma waves (kappa-band) above 80 Hz(typically detectable with sub-cranial EEG and/ormagnetoencephalograophy) can be used in inverse model-based enhancementof the frequency responses to the stimuli.

Various embodiments of the present invention recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular embodiments,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular embodiments,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various embodiments,the synchronous response may be determined for multiple subjectsresiding in separate locations or for multiple subjects residing in thesame location.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious embodiments, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular embodiments, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present invention recognize that significance measurescan be enhanced further using information from other modalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousembodiments, a cross-modality synthesis mechanism performs time andphase shifting of data to allow data from different modalities to align.In some examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various embodiments, time corrected GSR measuresare used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various embodiments, ERP measures are enhancedusing EEG time-frequency measures (ERPSP) in response to thepresentation of the marketing and entertainment stimuli. Specificportions are extracted and isolated to identify ERP, DERP and ERPSPanalyses to perform. In particular embodiments, an EEG frequencyestimation of attention, emotion and memory retention (ERPSP) is used asa co-factor in enhancing the ERP, DERP and time-domain responseanalysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousembodiments, EOG and eye tracking is enhanced by measuring the presenceof lambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular embodiments, specific EEGsignatures of activity such as slow potential shifts and measures ofcoherence in time-frequency responses at the Frontal Eye Field (FEF)regions that preceded saccade-onset are measured to enhance theeffectiveness of the saccadic activity data.

According to various embodiments, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular embodiments, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present invention recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

Integrated responses are generated at 611. According to variousembodiments, the data communication device transmits data to theresponse integration using protocols such as the File Transfer Protocol(FTP), Hypertext Transfer Protocol (HTTP) along with a variety ofconventional, bus, wired network, wireless network, satellite, andproprietary communication protocols. The data transmitted can includethe data in its entirety, excerpts of data, converted data, and/orelicited response measures. According to various embodiments, data issent using a telecommunications, wireless, Internet, satellite, or anyother communication mechanisms that is capable of conveying informationfrom multiple subject locations for data integration and analysis. Themechanism may be integrated in a set top box, computer system, receiver,mobile device, etc.

In particular embodiments, the data communication device sends data tothe response integration system. According to various embodiments, theresponse integration system combines analyzed and enhanced responses tothe stimulus material while using information about stimulus materialattributes. In particular embodiments, the response integration systemalso collects and integrates user behavioral and survey responses withthe analyzed and enhanced response data to more effectively measure andtrack neuro-responses to stimulus materials. According to variousembodiments, the response integration system obtains attributes such asrequirements and purposes of the stimulus material presented.

Some of these requirements and purposes may be obtained from a varietyof databases. According to various embodiments, the response integrationsystem also includes mechanisms for the collection and storage ofdemographic, statistical and/or survey based responses to differententertainment, marketing, advertising and otheraudio/visual/tactile/olfactory material. If this information is storedexternally, the response integration system can include a mechanism forthe push and/or pull integration of the data, such as querying,extraction, recording, modification, and/or updating.

The response integration system can further include an adaptive learningcomponent that refines user or group profiles and tracks variations inthe neuro-response data collection system to particular stimuli orseries of stimuli over time. This information can be made available forother purposes, such as use of the information for presentationattribute decision making According to various embodiments, the responseintegration system builds and uses responses of users having similarprofiles and demographics to provide integrated responses at 611. Inparticular embodiments, stimulus and response data is stored in arepository at 613 for later retrieval and analysis.

According to various embodiments, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 7 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 7 may beused to implement a data analyzer.

According to particular example embodiments, a system 700 suitable forimplementing particular embodiments of the present invention includes aprocessor 701, a memory 703, an interface 711, and a bus 715 (e.g., aPCI bus). When acting under the control of appropriate software orfirmware, the processor 701 is responsible for such tasks such aspattern generation. Various specially configured devices can also beused in place of a processor 701 or in addition to processor 701. Thecomplete implementation can also be done in custom hardware. Theinterface 711 is typically configured to send and receive data packetsor data segments over a network. Particular examples of interfaces thedevice supports include host bus adapter (HBA) interfaces, Ethernetinterfaces, frame relay interfaces, cable interfaces, DSL interfaces,token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as data synthesis.

According to particular example embodiments, the system 700 uses memory703 to store data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present embodiments are to be consideredas illustrative and not restrictive and the invention is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

What is claimed is:
 1. An apparatus, comprising: a flexible band; afirst hub coupled to a first end of the flexible band; a first electrodeextending from the first hub a first distance; a second electrodeextending from the first hub a second distance, the second distancedifferent than the first distance, wherein the first hub is rotatable onthe first end of the flexible band to change a position of the firstelectrode and the second electrode; a second hub coupled to a second endof the flexible band; a third electrode extending from the second hub athird distance; a fourth electrode coupled to the second hub a fourthdistance, the fourth distance different than the third distance; a clocksignal receiver to receive a clock signal representing timinginformation of a stimulus material; and a processor to synchronize thetiming information with neuro-response data gathered from at least oneof the first electrode, the second electrode, the third electrode or thefourth electrode.
 2. The apparatus of claim 1, wherein the clock signalreceiver comprises at least one of a cell tower signal receiver or asatellite signal receiver.
 3. The apparatus of claim 1 furthercomprising a stimulus material presentation device to present thestimulus material to a user, wherein the clock signal is transmittedfrom the stimulus material presentation device.
 4. The apparatus ofclaim 1 further comprising a data analyzer to: analyze neuro-responsedata gathered from at least one of the first, second, third or fourthelectrode while communicatively coupled to a head of a user exposed tostimulus material; and determine an effectiveness of the stimulusmaterial based on the analyzed neuro-response data.
 5. The apparatus ofclaim 4, wherein the data analyzer is to analyze the timing informationcorrelating the stimulus material and the neuro-response data, and theeffectiveness is further based on the timing information.
 6. Theapparatus of claim 1 further comprising a transmitter to transmitneuro-response data gathered from at least one of the first, second,third or fourth electrode to a remote analyzer.
 7. The apparatus ofclaim 6, wherein the transmitter is a wireless transmitter.
 8. Theapparatus of claim 1 further comprising a fifth electrode extending afifth distance from a midportion of the flexible band between the firsthub and the second hub.
 9. The apparatus of claim 8 further comprising asixth electrode extending a sixth distance from the midportion of theflexible band between the first hub and the second hub, the sixthdistance different than the fifth distance.
 10. The apparatus of claim1, wherein a first plurality of electrodes, including the firstelectrode and the second electrode, individually radiate from the firsthub.
 11. The apparatus of claim 10, wherein a second plurality ofelectrodes, including the third electrode and the fourth electrode,individually radiate from the second hub.
 12. The apparatus of claim 11,wherein the first plurality of electrodes and the second plurality ofelectrodes are arranged to avoid a temporal region of a head of a user.13. The apparatus of claim 1, wherein at least one of the first, second,third or fourth electrodes includes an amplifier to conditionneuro-response signals gathered by the at least one of the first,second, third or fourth electrodes.
 14. The apparatus of claim 13,wherein each of the first, second, third and fourth electrodes includesa respective amplifier to condition neuro-response signals gathered bythe respective electrode.
 15. The apparatus of claim 1 furthercomprising a flexible printed circuit board to communicateneuro-response signals gathered by the at least one of the first,second, third or fourth electrodes.
 16. The apparatus of claim 1,wherein a first number of electrodes at the first hub matches a secondnumber of electrodes at the second hub to counterbalance a first forcefrom the first hub against a head of a subject wearing the apparatuswith a second force from the second hub against the head.
 17. Theapparatus of claim 16, wherein the first number of electrodes isdisposed in a first pattern and the second number of electrodes isdisposed in a second pattern.
 18. The apparatus of claim 17, wherein thefirst pattern and the second pattern are symmetrical about a midpoint ofthe flexible band between the first hub and the second hub.
 19. Theapparatus of claim 1, wherein each of the first, second, third andfourth electrode is flexibly coupled to the respective hub.